Outer membrane vesicles (OMVs) and their therapeutic potential as anti-infectious agents
Bibliographic record
Abstract
Outer membrane vesicles (OMVs) derived from Gram-negative bacteria have emerged as promising therapeutic agents for combating infectious diseases. These small, spherical structures carry diverse cargo molecules, including virulence factors, antigens, and immunomodulatory molecules. OMVs derived from pathogenic bacteria can be engineered to deliver antimicrobial peptides, antibiotics, or specific antigens, eliciting targeted immune responses against bacterial pathogens. Similarly, OMVs can deliver viral antigens, facilitating immune recognition and clearance of viral infections. Furthermore, OMVs can be engineered to encapsulate antifungal agents or fungal antigens, enabling targeted delivery and immune activation against fungal pathogens. The cargo-carrying capacity and immunostimulatory properties of OMVs make them valuable tools for developing effective treatments against infectious diseases. This review also discusses the challenges and future directions in applying OMVs as therapeutic agents, highlighting the need for further research and development to harness their full potential in clinical applications. Overall, OMVs represent a promising avenue for developing novel therapeutic strategies against infectious diseases, offering targeted and immunomodulatory strategies for combating bacterial, viral, and fungal infections.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".